Systems, methods and computer-readable medium for determining depth-resolved physical and/or optical properties of scattering media by analyzing measured data over a range of depths

ABSTRACT

In depth-resolved imaging of scattering media, incident light interacts with tissue in a complex way before the signal reaches the detector: Light interacts with media between the light source and a specific depth, then scatters at that depth and the backscattered light again interacts with media on its way to the detector. The resulting depth-resolved signal therefore likely does not directly represent a physical or optical property of the media at those depths. Exemplary systems, methods and computer-accessible medium can determine physical or optical properties based on such depth-resolved signals. For example, almost all the light can interact with the media, and that the energy of the incident light at a certain depth is likely therefore related to the integral of the scattered light from all deeper locations. Based on the detected signals, the properties of the media can be estimated in an iterative way.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims priority from U.S. Patent Application Ser. No. 61/480,869 filed Apr. 29, 2011, and U.S. Patent Application Ser. No. 61/585,916 filed Jan. 12, 2012, the entire disclosures of which are incorporated herein by reference.

FIELD OF THE DISCLOSURE

The present disclosure relates to a determination of physical and/or optical information regarding a sample, and more particularly to exemplary embodiments of systems, methods and computer-readable medium for determining depth-resolved physical or optical properties of scattering media by analyzing measured data over a range of depths.

BACKGROUND INFORMATION

Glaucoma is the second leading cause of blindness worldwide. The clinical diagnosis of glaucoma is primarily based on characteristic patterns of visual field toss, progressive retinal nerve fiber layer (RNFL) thinning and optic nerve head (ONH) changes. Historically, the focus of glaucoma detection has been on the RNFL thickness. Several devices employing different imaging modalities, e.g., confocal laser ophthalmoscopy, scanning laser polarimetry and optical coherence tomography (OCT), produce measurements of these thicknesses. Raw OCT data, however, provides local measurements of scattering properties of the tissue and may therefore produce additional measures of the RNFL.

U.S. Patent Publication No. 2010/0208201 described segmenting the intensity data and assigning a single representative intensity to a segmented portion, and displaying a 2-D image of the assigned intensity. With respect to glaucoma diagnosis, the reflectivity of the RNFL has been shown to decrease in glaucoma (as described in Van der Schoot J, et al. IOVS 2010; 51:ARVO E-Abstract 212; Vermeer K A, et at, IOVS 2011; 52:ARVO E-Abstract 3666), especially in its early stages. Segmentation procedures were previously described in U.S. Pat. No. 7,782,464 to identify the boundaries between anatomical layers.

However, none of such described systems, methods and procedures describe a way to normalize the data to take into account instrument errors, ocular opacities, etc

Several techniques are used for depth-resolved imaging of scattering media, such as confocal microscopy and/or optical coherence tomography (OCT). In these techniques, incident light travels through the media, interacts with the media (e.g., an anatomical sample), and is collected by one or more detectors. The interaction of the light and the media can be complex, because interaction does not only take place at a single depth. Instead, the incident bundle generally interacts with many and/or all layers it passes through, scatters at some depth and the scattered beam again interacts with the media until it arrives at the detector.

For example, with OCT techniques, the sample is probed by a coherent light source and the depth-resolved backscatter signal intensity is recorded. The exemplary OCT techniques can be implemented in many ways, with fixed or moving reference mirrors, with spectrometers or swept-source systems, etc. In such cases, however, the OCT signal is generally dependent on energy of the backscattered beam that reaches the detector. Many of these measurements along a line are then combined to produce an image, as shown in FIG. 1.

In an exemplary OCT image, the intensity that is measured from a certain depth can be gray scale coded, where white can indicate a strong signal, and black—a weak or no signal. The OCT beam is generally incident from above on the tissue. Unfortunately, these images likely do not reflect the physical or optical properties of the tissue. Instead, they only illustrate the result of the complex interaction, which can mean that the same tissue may appear differently (i.e., with different signal intensity, illustrated by the different signal intensity of the RPE at the locations indicated by arrows 20 in FIG. 1) at different locations, determined by how the surrounding tissue is structured. This is because the exemplary OCT signal depends not only on the optical properties of the media at some depth, which result in the backscattered signal. Instead, the exemplary OCT signal is also dependent on the strength of the incident beam at that location, which is affected by the media it passes through first. In addition, the resulting backscattered beam again has to pass through some part of the media before it reaches the detector and is therefore further attenuated. This can result in artifacts that are frequently observed in OCT images. One example is the shading of blood vessels. Because the blood vessels cause a large reduction of the intensity of the incident light beam, the scatter intensity at deeper locations is largely reduced and is further attenuated on the way back to the detector. This can result in apparent gaps of underlying tissue, which clearly does not mimic the tissues structure (see FIG. 1, arrows 10). Another artifact is the very dim appearance of the choroid and sclera, both scattering tissue types, due to the attenuation of the incident light beam in other highly scattering layers, especially a retinal nerve fiber layer (RNFL) and the retinal pigment epithelium (RPE). Yet another artifact is the reduced intensity in the image in case of floaters or media opacities (e.g. in the cornea, the lens or the vitreous), which attenuates the power of the incident beam.

Further, because the measurements are the result of this complex interaction, the signal strength corresponding to a single depth measurement does not directly represent a physical or optical property of the medium at that depth. Instead, only morphological features of the measurements, often visualized in an image, are evaluated. However, these morphological features also depend on the signal strength and are therefore not always clearly defined in OCT images.

According to an exemplary embodiment of the present disclosure, it is possible to determine physical and/or optical properties of the medium from the measurements. For this determination, information of other, deeper locations can be included in the reconstruction process. An example of such a reconstruction is the determination of attenuation coefficients from OCT data. In this case, OCT data from both nearer and deeper locations are used to determine, iteratively, the local scattering intensity and the local attenuation coefficient.

In ophthalmology, OCT procedures have been used to image the retina for a number of years. Typically, a measurement is defined as an A-line, which contains depth-resolved backscatter data at a single transverse location of the retina. By using scanning optics, many A-lines are recorded along a transverse path, e.g. a straight line or a circle. In the past few years, it has gained in popularity due to increased scanning speeds, which facilitates an acquisition of volumetric three-dimensional (3D) scans by performing a raster-scan across the retina.

Not all data in an OCT scan is useful for the clinical task at hand. For example, in glaucoma, the RNFL can be the tissue layer that is of most interest. Segmentation procedures can be employed to segment the NFL in OCT data For compliance with conventional tests and because of easy interpretation, a segmented OCT scan can then be reduced to one RNFL thickness measurement for each A-line in the data set. In case of a two-dimensional (2D) data set, such as a circular scan around the papilla, this can result in a plot of the angle of the circle against the thickness at that location. Such plot can be called a TSNIT-plot. In a case of a 3D data set, such as a raster-scan of the peripappilary area, the result can be a thickness map, graphically showing the thickness of the RNFL at all scanned locations.

When reducing the segmented OCT data to these thicknesses (e.g., of a single tissue or multiple tissues) or distance (from one boundary of a tissue type to another boundary of the same or a different tissue type), the OCT data itself is not used. The produced data thus likely provide no information about the underlying tissue types. In case of glaucoma, the RNFL is known to deteriorate, tissue is lost and therefore the thickness of the RNFL decreases. However, the backscattering properties of the deteriorating RNFL may be different than that of healthy RNFL. Simply processing the absolute measurements will produce unreliable results. For example, media opacities may result in lower measured backscattering, which is not due to the measured tissue itself. Therefore, these measurements must be normalized by calculating the ratio of the measured backscatter to the backscatter of an unaffected structure with uniform scattering properties, such as the retinal pigment epithelium (RPE).

Accordingly, there is a need to address at least some of the deficiencies described herein above.

SUMMARY OF EXEMPLARY EMBODIMENTS

At least some of such deficiencies can be address with exemplary embodiments of the present disclosure providing systems, methods and computer-readable medium for determining depth-resolved physical or optical properties of scattering media by analyzing measured data over a range of depths.

According to certain exemplary embodiments of the present disclosure, a measurements of a single layer (and/or a combination of layers) can be calculated relative to the measurements of another layer (and/or a combination of layers), such that instrument errors, ocular opacities and other artifacts can be effectively canceled. These normalized values can then be used for further processing, e.g. to produce a diagnostic score or to visualize the values as a map, e.g., for the assessment of Glaucoma.

In depth-resolved imaging of scattering media, incident light interacts with tissue in a complex way before the signal reaches the detector. For example, light interacts with media between the light source and a specific depth, then scatters at that depth and the backscattered light again interacts with media on its way to the detector. The resulting depth-resolved signal therefore likely does not directly represent a physical or optical property of the media at those depths. According to one exemplary embodiment of the present disclosure, systems, methods and computer-accessible medium can be provided to determine physical or optical properties based on such a depth-resolved signal. For example, almost all the light can interact with the media, and that the energy of the incident light at a certain depth is likely therefore related to the integral of the scattered light from all deeper locations. Based on the detected signals, the properties of the media can be estimated in an iterative way. The exemplary system, method and computer-accessible medium can be used together with, e.g., retinal optical coherence tomography data, facilitating the calculation of depth-resolved attenuation coefficients. It is possible to, e.g., transform data resulting from complex interactions of light and media at a range of depths into data representing a decoupled physical or optical property of the tissue at a range of depths.

Thus, the exemplary systems, methods and computer-accessible medium can analyze the imaging process, thereby modeling the process of the interaction between the incident light beam and the tissue, resulting in the OCT measurements. Subsequently, the inverse problem can be solved to produce, for example, local attenuation coefficients from the OCT data. The resulting image can represents a physical and/or optical property of the local media that can be free of some or many of the artifacts in the original OCT data set. Because the exemplary image shows tissue properties rather than the result of complex interactions, the signal strength of the tissue can be largely independent of the structure of surrounding tissue layers. The resulting exemplary image can therefore be better suited for image processing, likely resulting. for example, in a segmentation of tissue layers. In addition, these physical or optical tissue properties can be useful for diagnosis and monitoring of disease and/or disease progression.

According to certain exemplary embodiments of the present disclosure, systems, methods and computer-accessible medium can be provided for determining at least one property of at least one biological structure. For example, with such exemplary systems, methods and computer-accessible medium, it is possible to obtain a plurality of signals received at particular depths within the biological structure(s). At least first one of the signals can be obtained from a first depth of the particular depths, and at least second one of the signals can be obtained from a second depth of the particular depths. The first and second depths can be different from one another. In addition, it is possible to determine information based on the signals and an assumed property of the biological structure(s). Further, at least one calculated property can be calculated based on the information by excluding at least a portion of the information associated with the signals provided from the particular depths that are closer than a predetermined depth within the biological structure(s), where the calculated property is an attenuation property.

According to one exemplary embodiment of the present disclosure, the determinations of the information and the calculated property can be reiterated at least once, such that, when the information is determined, the assumed property can be replaced with the calculated property of the determination of the calculated property to obtain the property of the biological structure(s). The signals can be optical coherence tomography signals or ultrasound signals. The calculated property can includes local optical properties of scattering media of the biological structure(s), and the local optical properties can be determined using the information from a range of the particular depths. For example, the local optical properties can be determined using the information obtained from the depth which is a shallower depth and the information obtained at the second depth which is a larger depth. It is also possible to sum the information obtained from the second depth to obtain an estimate of an intensity of a radiation forwarded to the biological structure(s).

For example, the local optical property can include an attenuation coefficient. The calculated property can include at least one optical property or at least one physical property which are iteratively determined from the information to be estimate at various depths within the structure(s). The calculated property can be used for diagnosis or for at least one of a manual segmentation or an automatic segmentation. Further, the calculated property can include a calculated attenuation, and the assumed property can be an assumed attenuation.

According to another exemplary embodiment of the present disclosure, the information can be based on a local backscattered energy from the at least one structure. The local backscattered energy can be measured by an optical coherence tomography procedure.

In another exemplary embodiment of the present disclosure, method, system and computer-accessible medium can be provided to obtain tissue-specific backscatter properties from OCT data, to normalize the tissue specific backscatter properties, to analyze these properties and/or to score these properties and/or to display these properties in an image. In one exemplary embodiment, after OCT data is obtained, the tissue (or set of tissues) of interest can be segmented manually or by an exemplary segmentation procedure. The exemplary segmentation procedure can provide and/or generate, e.g., a boundary between tissue types and/or anatomical layers. Subsequently, by selective combining (e.g., through averaging) of the depth-resolved backscatter data, each recorded A-line can result in a measurement that can describe the backscatter properties of the selected tissue. Combining the data can be performed by averaging the data over the selected depths, and/or obtained using certain exemplary statistical procedures to determine other statistical features of the data, e.g., variance.

In one exemplary embodiment, to make the data robust against an exemplary measurement system induced variations or variations associated with the optical properties of a particular eye, such as opacities in the lens or vitreous, this data can also be normalized. The data can be normalized by, for example, calculating the ratio of the average of the tissue type of interest and a reference structure, such as a tissue type or anatomical layer like for instance the retinal pigment epithelium (RPE). This can effectively cancel errors, e.g., instrument errors, ocular opacities and/or other artifacts. The normalized data can then be presented numerically, such that the outcome is related to the disease. For example, in diagnosis of glaucoma, an average of the normalized RNFL backscatter signal can be calculated and compared to known distributions of that average for healthy and glaucomatous eyes. The normalized backscatter signal can be analyzed with respect to sectors, e.g., around or at an optic nerve head or a macula, where each sector can receive, e.g., an individual score. In addition, the resulting, normalized data can be visualized in a similar way as the thickness data (e.g., via a plot for a line scan and/or in an image for a raster-scan). For example, by determining normal ranges of the backscatter properties of the tissue, a pathological tissue can be identified automatically by its abnormal backscatter properties.

The exemplary embodiments have numerous applications various domains where OCT data is inhomogeneous, such as, e.g., skin tissue, blood vessels, etc.

According to another exemplary embodiment of the present disclosure, system, method and computer-accessible medium can be provided for generating a partial image from a 3-D intensity data set corresponding to the distribution of reflection sites within the eye acquired by scanning an eye with an exemplary optical coherence tomographic (OCT) device. Using such exemplary embodiment, it is possible to segment the intensity data to identify a first pair of spaced apart reference surfaces. The intensity data along a Z-axis extending between this first pair of surfaces can be processed to assign a first representative intensity value at each of a plurality of X and Y positions. Further, the intensity data can be segmented to identify a second pair of spaced apart references surfaces. Then, the intensity data along a Z-axis extending between this second pair of surfaces can be processed to assign a second representative intensity value at each of a plurality of X and Y positions. The first and second representative values can be transformed to a single normalized representative value at each of a plurality of X and Y positions.

For example, a numerical score can be assigned to locations of the eye based on the normalized representative value, and/or to the whole of the volumes defined by the pairs of surfaces. The normalized representative values can be displayed as an image. It is also possible to process the intensity data along a Z-axis extending between a pair of the surfaces to assign a single representative intensity value for all X and Y positions, e.g., for each pair. The segmentation of the data can be automatically performed based on a procedure provided by a computer program and/or manually. The segmented data can be generated by, e.g., associating features with every data point based on the intensity data of that data point and other data points within the same intensity data set, and identifying the reference surfaces by application of a machine learning classifier based on these features. One of the reference surfaces can correspond to an anatomical layer and/or a retinal layer. Further, an exemplary two dimensional (2D) image can be used to register a three-dimensional (3D) OCT cross-section image. A machine learning classifier which is a support vector machine can be used. The output of the machine learning classifier can be further processed to produce the reference surfaces.

These and other objects, features and advantages of the present disclosure will become apparent upon reading the following detailed description of exemplary embodiments of the present disclosure, when taken in conjunction with the appended drawings and claims provided herewith.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects, features and advantages of the present disclosure will become apparent from the following detailed description taken in conjunction with the accompanying drawings showing illustrative embodiments of the present disclosure, in which:

FIG. 1 is an exemplary OCT scan of a healthy eye;

FIG. 2 is an exemplary attenuation coefficient image produced by processing the OCT image according to exemplary embodiments of the present disclosure;

FIG. 3 is a flow diagram of a method according to an exemplary embodiment of the present disclosure;

FIG. 4 is a flow diagram of an analysis procedure according to an exemplary embodiment of the present disclosure;

FIG. 5A is an exemplary SD-OCT image of a normal eye obtained using an exemplary embodiment of the system, method and/or computer-accessible medium in accordance with the present disclosure;

FIG. 5B is an exemplary SD-OCT image of an advanced glaucomatous eye obtained using the exemplary embodiment of the system, method and/or computer-accessible medium in accordance with the present disclosure;

FIG. 6A is a graph of a reflectivity of the RNFL, and the GCIPL of the sample obtained using the exemplary embodiment of the system, method and/or computer-accessible medium in accordance with the present disclosure;

FIG. 6B is a graph of a ratio between the RNFL and the GCIPL of the sample obtained using the exemplary embodiment of the system, method and/or computer-accessible medium in accordance with the present disclosure;

FIG. 7 is a graph of an average normalized RNFL reflectivity on a band around the optic nerve head for normal and glaucomatous eyes;

FIG. 8 is a graph of an average normalized RNFL reflectivity in different quadrants for normal and glaucomatous eyes;

FIG. 9 is a set of exemplary images of normalized RNFL reflectivity maps of normal and glaucomatous eyes obtained using the exemplary embodiment of the system, method and/or computer-accessible medium in accordance with the present disclosure; and

FIG. 10 is a diagram of a system according to an exemplary embodiment of the present disclosure which can perform and/or execute the exemplary procedures and method described herein.

Throughout the drawings, the same reference numerals and characters, if any and unless otherwise stated, are used to denote like features, elements, components, or portions of the illustrated embodiments. Moreover, while the subject disclosure will now be described in detail with reference to the drawings, it is done so in connection with the illustrative embodiments. It is intended that changes and modifications can be made to the described exemplary embodiments without departing from the true scope and spirit of the subject disclosure and appended claims provided herewith.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS Exemplary Procedure

The presented method models the interaction of light with the medium and the resulting signal at the detector and then solves the inverse problem iteratively to locally calculate the physical or optical properties of the medium.

Exemplary Model

Exemplary Attenuation of Incoming Beam

During an exemplary propagation thereof through a locally homogeneous layer, the power of the incoming beam is attenuated according to:

dL(x)=−μ(x)L(x)dx,   (1)

where L(x) is the power of the beam at depth x and μ is the attenuation coefficient of the layer. Solving this, with boundary condition L(0)=L₀ to define the power of the incoming beam, results in the following equation for the attenuated beam:

$\begin{matrix} {{L(x)} = {L_{0}^{- {\int_{0}^{x}{{\mu {(x)}}\ {x}}}}}} & (2) \end{matrix}$

which, for a constant attenuation coefficient, reduces to L(x)=L₀e^(−μx). An exemplary calculation of the power of the beam L(x) at location x in equation 2 provides that the attenuation coefficients up to position x are known, and that the power of the incident beam L₀ is known.

Exemplary Local Incident Light Power and Integration

Another way to define the power of the incoming beam is by analyzing the attenuated power A(x)

dA(x)=−dL(x)=μ(x)L(x)dx.   (3)

Integrating this equation and using the fact that all power can eventually be attenuated by the medium results in

$\begin{matrix} {{A(x)} = {{\int_{0}^{x}{{\mu (x)}{L(x)}\ {x}}} = {{{\int_{0}^{\infty}{{\mu (x)}{L(x)}\ {x}}} - {\int_{x}^{\infty}{{\mu (x)}{L(x)}\ {x}}}}\  = {L_{0} - {\int_{x}^{\infty}{{\mu (x)}{L(x)}\ {{x}.}}}}}}} & (4) \end{matrix}$

The (remaining) power of the incoming beam can be defined by

$\begin{matrix} {{L(x)} = {{L_{0} - {A(x)}} = {\int_{x}^{\infty}{{\mu (x)}{L(x)}\ {{x}.}}}}} & (5) \end{matrix}$

In contrast to Equation 2, this exemplary formulation of L(x) does not require that L₀ is known. What should be known is the attenuation coefficient at location x and deeper, and the intensity L(x) at location x and deeper.

Relating Exemplary Attenuation and Backscatter

An exemplary attenuation can result from both scattering and absorption. If a fixed fraction a of the attenuated light is backscattered, the energy density of the backscattered light at depth x can be given by

$\begin{matrix} {{B(x)} = {{\alpha \frac{A}{x}} = {{{\alpha\mu}(x)}{{L(x)}.}}}} & (6) \end{matrix}$

Exemplary OCT Signal

Such exemplary backscattered may not be the signal that is actually measured by the exemplary OCT system and/or procedure. Instead, the signal can again be attenuated by the tissue on the way back to the tissue surface before reaching the detector, where S(x) describes the intensity of light that scattered at depth x and subsequently reaches the tissue surface:

$\begin{matrix} {{S(x)} = {{B(x)}^{- {\int_{0}^{x}{{\mu {(x)}}\ {x}}}}}} & (7) \end{matrix}$

where I(x) is the light intensity, that after detection by a detector and multiplied by a conversion factor β, provided the exemplary measured digital signal,

$\begin{matrix} {{I(x)} = {{\beta \; {S(x)}} = {\beta \; {B(x)}^{- {\int_{0}^{x}{{\mu {(x)}}\ {x}}}}}}} & (8) \end{matrix}$

Exemplary determination of

To calculate μ(x), we write, using Equation 5,

$\begin{matrix} {{\mu (x)} = {\frac{{\mu (x)}{L(x)}}{L(x)} = {\frac{{\mu (x)}{L(x)}}{\int_{x}^{\infty}{{\mu (x)}{L(x)}\ {x}}} = {\frac{{{\alpha\mu}(x)}{L(x)}}{\int_{x}^{\infty}{{{\alpha\mu}(x)}{L(x)}\ {x}}}.}}}} & (9) \end{matrix}$

Substituting Equation 6 and including β in both the numerator and the denominator yields

$\begin{matrix} {{\mu (x)} = {\frac{B(x)}{\int_{x}^{\infty}{{B(x)}\ {x}}} = {\frac{\beta \; {B(x)}}{\int_{x}^{\infty}{\beta \; {B(x)}\ {x}}}.}}} & (10) \end{matrix}$

Because the penetration of light in the tissue is limited to e.g, the image depth D, the calculation of μ(x) is approximated by

$\begin{matrix} {{\mu (x)} \approx {\frac{\beta \; {B(x)}}{\int_{x}^{D}{\beta \; {B(x)}\ {x}}}.}} & (11) \end{matrix}$

Exemplary Iterative Procedure

Although the exemplary analysis may not provide a way to directly calculate μ(x) from the OCT image data I(x), according to exemplary embodiments of the present disclosure can calculate/determine the backscattered signal from the image data and from μ(x), and determine such μ(x) from the backscattered signal. Given that both μ(x) and βB(x) are unknown, it is unlikely to directly calculate the attenuation coefficients. Instead, an exemplary numerical procedure can be implemented to estimate both quantities.

One exemplary procedure to perform such estimation can include a calculation of μ(x) from the OCT image data I(x) by, e.g., an iterative routine, an exemplary embodiment of which is shown in a flow diagram of FIG. 3. In this exemplary procedure illustrated in FIG. 3, the attenuation coefficients μ(x) can be initialized by a small value. Based on these initial values and the image data I(x), βB(x) can be calculated by Equation 8. Then, based on the calculated βB(x), μ(x) can be calculated by Equation 11. This exemplary procedure is repeated until it converges. This exemplary procedure can be further described, with reference to FIG. 3, as follows:

-   -   1. Initialize μ⁽⁰⁾(x) to a small number (block 310) and set k to         0 (block 320)     -   2. k→k+1 (block 330)     -   3. Calculate βB^((k))(x) from μ^((k−1))(x) and I(x) (Equation 8)         (block 340)     -   4. Calculate μ^((k))(x) from βB^((k))(x) (Equation 10 (block         350)     -   5. Determine if a convergence occurred (block 360), and if not,         repeat procedures 2-5 (blocks 330-360) until the convergence is         reached     -   6. Determine μ(x)←μ^((k))(x) (block 370)

The exemplary local attenuation μ(x) can be given by μ^((k))(x) The exemplary convergence can be defined in various exemplary ways. For example, a fixed number of iterations can be used. Alternatively, the size of the update step can be analyzed, and convergence can be assumed when it is below some absolute or relative value.

Exemplary Discretization

An exemplary mathematical formulation indicated herein can be usable in the continuous case. Certain real-life measurements can be discrete, and therefore an exemplary discrete set of equations should be derived. Various exemplary discretizations can be used, each based on different assumptions. One such exemplary discretization is described as follows.

For example, constant measurement intervals, centered on the sample points and with a constant spacing of Δ_(x) can be assumed. The discrete version of I(x), I[i] , can then be defined as

$\begin{matrix} {{{I(x)} = {I\lbrack i\rbrack}},{{{where}\mspace{14mu} x} = {\left( {i + \frac{1}{2}} \right){{\Delta \;}_{x}.}}}} & (12) \end{matrix}$

Similarly, the discrete version of μ(x) can be defined. For example, replacing the integral of Equation 8 by its discrete version by assuming constant μ(x) over the pixel size can result in

$\begin{matrix} {{I\lbrack i\rbrack} = {\beta \; {B\lbrack i\rbrack}^{- {\Delta_{x}{({{\frac{1}{2}{\mu {\lbrack i\rbrack}}} + {\sum\limits_{j = 0}^{i - 1}\; {\mu {\lbrack j\rbrack}}}})}}}}} & (13) \end{matrix}$

and solving for βB[i] yields

$\begin{matrix} {{\beta \; {B\lbrack i\rbrack}} = {{I\lbrack i\rbrack}{^{\Delta_{x}{({{\frac{1}{2}{\mu {\lbrack i\rbrack}}} + {\sum\limits_{j = 0}^{i - 1}\; {\mu {\lbrack j\rbrack}}}})}}.}}} & (14) \end{matrix}$

Similarly, the exemplary calculation of the discrete attenuation coefficient μ[i] can be given by

$\begin{matrix} {{\mu \lbrack i\rbrack} = {\frac{\beta \; {B\lbrack i\rbrack}}{\Delta_{x}\left( {{\frac{1}{2}\beta \; {B\lbrack i\rbrack}} + {\sum\limits_{j = {i + 1}}^{D - 1}\; {\beta \; {B\lbrack j\rbrack}}}} \right)}.}} & (15) \end{matrix}$

Further Exemplary Description

According to further exemplary embodiments of the present disclosure, the system, method and computer-accessible medium can be further modified including additional effects. For example, in certain exemplary OCT procedures and systems, the limited coherence length can result in a reduced signal for depths at an increasing distance from the so-called zero-delay line (which can be determined by the position of the static mirror). This signal fall-off can be modeled by an exponential function and/or another decay function, and included in the exemplary procedure and/or system. The limited depth-of-focus can be modeled in a similar way, where the exact focus parameters are taken into account to correct for the collection efficiency of the light over the axial position in the focus. Another exemplary modification of the exemplary system, method and computer-accessible medium according to the present disclosure can include the treatment of noise. For example, a small value, which can be based on shot-noise calculations or on a reference measurement describing the system noise, can be subtracted from the OCT data to reduce the accumulation of noise in regions with little scattering signal. Using multiple scattered light can also result in a background signal. This contribution can be modeled and accounted for by subtraction according to the exemplary embodiments of the present disclosure. Data of neighboring pixels can be combined to get a better estimate of the local scattering signal. Various regularization methods known to those having ordinary skill in the art can be used on the estimation of both βB(x) and μ(x), thereby incorporating prior knowledge about the structure of the tissues.

An exemplary initialization of μ(x) can be performed in several ways. According to one exemplary procedure, the initialization can be done by initializing μ(x) with a small number for every x. This exemplary small number can be chosen such that the total attenuation over the image depth D is large, for example, 99.9%. For constant μ(x), Equation 2 can reduce to L(x)=L₀e^(−μx). Evaluating this equation at x=D and rearranging the equation results in

$\mu = {{- \frac{1}{D}}\log {\frac{L(d)}{L_{o}}.}}$

Setting the ratio

$\frac{L(D)}{L_{o}}$

to 0.001 to match the 99.9% attenuation then results in the initial value for μ.

In other cases, a prior knowledge about the expected values for μ(x) may be available, for example, as being obtained from mean values of a large data set. Using such further estimates to initialize μ(x) can result in a faster convergence of the exemplary procedure.

Further Example

While the systems, methods and computer accessible medium according to exemplary embodiments of the present disclosure can be more generally applicable, attention is drawn to an exemplary illustration in FIG. 1 showing an exemplary retinal OCT image 10 of a healthy human eye. As indicated in FIG. 1, blood vessels can cause severe shading of underlying tissue (arrows 10) and layers of tissue that are presumably homogeneous show varying brightness (RPE, arrows 20).

According to one exemplary embodiment of the present disclosure, processing the exemplary data from FIG. 1 using the systems, methods and computer accessible medium according to exemplary embodiments of the present disclosure can result in an attenuation coefficient image, as shown in FIG. 2.

For example, the exemplary attenuation coefficient image shown in FIG. 2 can be generated by processing the OCT image according to certain exemplary embodiments of the present disclosure. Blood vessels would likely result in almost no shading, and the RPE is shown as a uniformly bright layer, the choroid and sclera are depicted as realistically highly attenuating tissues and the pixel brightness has a physical meaning (as shown in FIG. 2 using grey scale bar). The shadowing due to blood vessels can be largely removed and the RPE has a more uniform appearance. FIG. 2 further illustrates the scattering properties of the choroid and the sclera. The noisy appearance can be due to the small amount of incident light remaining after passing through the retinal layers.

Additional Exemplary Procedure

According to still another exemplary embodiment of the present disclosure, a further procedure can be provided, as illustrated in a flow diagram of FIG. 4. For example, as shown in FIG. 4, data can be acquired in block 405, OCT data (e.g., image OCT data) of the retinal sample can be determined/obtained from the acquired data in block 410, and at least some of such OCT data can be segmented in block 420 with respect to the retinal layers. The actual OCT data from block 410 and the segmented data from block 420 (which is then forwarded via block 430) can then be combined in block 440 to produce an exemplary property, e.g., an average intensity or a variance from a layer. Non-normalized layer data from the property determination can he normalized in block 450 on second layer data to generate normalized data in block 460. The normalized data can he processed into numerical results in block 470, such as values for a certain retinal sector, and/or the normalized data can be processed into a graphical representation of the numerical results in block 480, such as an image. Provided below are further details regarding such exemplary procedures.

Exemplary Acquisition

For example, in one exemplary embodiment of the present disclosure, the acquisition of the data (in block 405) can be performed by an exemplary OCT system, such as, e.g., Spectralis OCT (see Heidelberg Engineering, Heidelberg, Germany), or an exemplary optical frequency domain interferometry (OFDI) system. The spectrally resolved interference data from such exemplary system(s) can be processed into image data (block 410). Multiple scans or A-lines can be averaged for improved SNR. It should be understood that any other exemplary OCT system, including those with different optics, wavelengths, sampling density or resolution, can be used with the exemplary embodiments of the present disclosure.

Exemplary Segmentation

The goal of the exemplary segmentation procedure (see block 420) can be to define coherent areas of similar tissue types in the OCT image. For example, one exemplary method for segmenting OCT data is described herein therefor, and it should be understood that other approaches can also be used. Such exemplary method is based on defining feature vectors for each OCT data point (or pixel), followed by automatic classification by a machine learning algorithm and then using an optional exemplary regularization procedure to produce smooth results. The method focuses on interfaces between different tissue types and the result of the segmentation is therefore ‘above’ or ‘below’ the interface of interest. By combining interfaces, a tissue can be segmented.

Exemplary Feature Vectors

The measured backscatter of a pixel provides insufficient data for its classification. For example, there are different tissues with similar backscattering properties, which may therefore be inseparable based on that property alone. However, combined with features based on backscatter of surrounding tissue, a unique labeling can be performed. Each pixel can therefore be augmented by data from surrounding areas of pixels, resulting in a feature vector for each pixel. The exemplary method can look at single A-lines at a time, although it is certainly possible to also take neighboring A-lines into account.

Classification of pixels can generally be performed based on one or more features of these pixels. In OCT data, one of the most basic features can he the value produced by the exemplary OCT measurement. However, e.g., given that a backscatter value may not be specific for any tissue, the data may not be segmented based on only that. For example, both the RNFL and the RPE are generally strongly backscattering layers in the retina.

Exemplary features can be defined as follows. First, as described herein, incorporating only the pixel value itself may be insufficient. Instead, data from pixels above and below a current one can be incorporated as well. Second, an exemplary interface can often be delineated by an increase or decrease of the OCT signal, resulting in an intensity edge in the B-scan. It is possible to define features based on individual A-lines. This can facilitate the use of e.g., the same features (and therefore classifiers) irrespective of the scan protocol (e.g., the number of A-lines per B-scan, or the number of B-scans per volume). Thus, it is possible to utilize, e.g., one dimensional Haar-like features. It is possible, according to one exemplary embodiment of the present disclosure, to incorporate averages and gradients, both on different scales. Haar-like features can be selected over, for example, Gaussian averages and differences, because of their fast implementation using, e.g., lookup tables.

For example, according to a procedure of one exemplary embodiment of the present disclosure, let the intensity along an A-line be denoted by f_(x,y)(z), where x and y are the lateral coordinates of the A-line and z is the depth or distance in axial direction. In the remainder, it is possible to skip the lateral coordinates, and write f(z). Then, the first feature, g⁰, can be to simply f itself:

g ⁰(z)=f(z).   (16)

Next, the averages g scale d are defined by simply averaging 2^(d) pixels centered on f

$\begin{matrix} {{g^{d}(z)} = {\frac{1}{2^{d}}{\sum\limits_{z^{\prime} = {1 - 2^{d - 1}}}^{1 + 2^{d - 1}}\; {{f\left( {z + z^{\prime}} \right)}.}}}} & (17) \end{matrix}$

Similarly, the gradient h⁰ can be calculated by

h ⁰(z)=f(z+1)−f(z)=g⁰(z+1)−g ⁰(z)   (18)

and the gradients h^(d) at scale d can be defined by

h ^(d)(z)=g ^(d)(z+2^(d−1))−g ^(d)(z−2^(d−1)),   (19)

Based on these exemplary features, we define the full feature vector x(z) up to scale d for each pixel as

x (z)=[g ⁰(z), g ¹(z),h ¹(z), . . . , g ^(d)(z),h ^(d)(z)].   (20)

The optimal number of scales can be tuned for different interfaces. In addition, further advanced features can be used, such as distance to the optic nerve head, distance to other (e.g., already segmented) layers, distance to other landmarks, averages and gradients centered at other locations and other statistical descriptions of the distribution of the intensities.

Exemplary Classification

An exemplary classifier can produce a label for each input or feature vector x. A specific type of the exemplary classifier can be a support vector machine (SVM). During training, this exemplary support vector machine can aim to generate a maximum margin between the classification boundary and the samples closest to this boundary. When given a new, unlabeled feature vector x, the exemplary support vector machine can evaluate

s( x )=

w, x

+b   (21.)

and can use the sign of s( x) to produce the label. For example,

•,•

denotes the inner product, w denotes the normal of the (linear) classification boundary and b is some offset. In the training stage, w is defined as a weighted sum of the training samples x _(i). Due to the way the exemplary support vector machine can be optimized, many of the weights may go to zero and effectively only a relatively small number of samples, the support vectors can be used to define w. Thus, equation (21) can be rewritten as

$\begin{matrix} {{{s\left( \overset{\_}{x} \right)} = {{\sum\limits_{i = 1}^{N}\; {{\overset{\_}{\alpha}}_{i}y_{i}{\langle{{\overset{\_}{x}}_{i},\overset{\_}{x}}\rangle}}} + b}},} & (22) \end{matrix}$

where α _(i) denotes the weight of training sample x _(i) and y_(i) denotes its corresponding label (±1).

The classifier of equation (22) can be a linear classifier, given that its result is a linear combination of the inner product of the feature vector x and the support vectors. However, by replacing the inner product by a kernel K(•,•) , a non-linear exemplary support vector machine can be constructed as follows:

$\begin{matrix} {{s\left( \overset{\_}{x} \right)} = {{\sum\limits_{i = 1}^{N}\; {{\overset{\_}{\alpha}}_{i}y_{i}{K\left( {{\overset{\_}{x}}_{i},\overset{\_}{x}} \right)}}} + b}} & (23) \end{matrix}$

Implicitly, the kernel can map the input features into a possibly very high dimensional space. In this feature space, a linear classification is performed. Various kernels can be used, such as, e.g., polynomial kernels or radial basis functions. In the latter case, the kernel can map the input features into an infinite dimensional space, giving highly non-linear classification boundaries. With polynomial kernels, the dimension of the feature space can be better controlled.

In general, the exemplary kernel-support vector machines, given by equation (23), may likely not be rewritten as an explicit linear function as in equation (21) or equation (22). The disadvantage of the implicit form of equation (23) can be that it uses the storage of all support vectors and, for every new sample, it needs to calculate the kernel for each support vector.

In some exemplary cases, however, the kernel can be written explicitly. This applies, for example, to the polynomial kernel K( x _(i), x _(j))=( x _(i)· x _(j)+1)^(d), where d is the degree of the polynomial. For higher order polynomial kernels, the exemplary resulting mapping can result in a highly dimensional feature space, but for lower order kernels (degree 2 and possibly 3), explicit calculation can be performed. This can be done by writing the kernel as an inner product of a mapping φ(•):

K( x _(i) , x _(j))=

φ( x _(i)),φ( x _(j))

  (24)

For example, for a polynomial kernel of degree 1, the corresponding exemplary mapping can be φ( x)=(1, x₁, . . . , x_(n))^(T), where x_(i) is the i-th element of vector x, containing n elements. In a similar way, explicit mappings can be found for polynomial kernels. If such exemplary explicit mapping φ(•) exists, equation 3 can be rewritten as

$\begin{matrix} {{s\left( \overset{\_}{x} \right)} = {{{\sum\limits_{i = 1}^{N}\; {\alpha_{i}y_{i}{K\left( {{\overset{\_}{x}}_{i},\overset{\_}{x}} \right)}}} + b} = {{{\sum\limits_{i = 1}^{N}\; {\alpha_{i}y_{i}{\langle{{\varphi \left( {\overset{\_}{x}}_{i} \right)},{\varphi \left( \overset{\_}{x} \right)}}\rangle}}} + b} = {{\langle{\overset{\_}{w},{\varphi \left( \overset{\_}{x} \right)}}\rangle} + b}}}} & (25) \end{matrix}$

yielding a similar result as equation (21). As a result, w can be precomputed and for new data x only the mapping φ( x) and its inner product with w can be calculated.

In an exemplary application according to one exemplary embodiment of the present disclosure, a polynomial kernel of degree 2 can be chosen, with the corresponding mapping

φ( x )=(1, √{square root over (2)} x ₁, . . . √{square root over (2)} x _(n) ,x ₁ ,x ₂ , . . . , x _(n−1) x _(n) ,x ₁ ² , . . . , x _(n) ²)   (26)

which transformed vector x from an n-dimensional space into an

$\frac{\left( {n + 2} \right)\left( {n + 1} \right)}{2}$

-dimensional space. By precomputing w, storing all support vectors may no be longer needed. In addition, the exemplary calculation can be much faster due to the linear operation of the resulting exemplary support vector machine.

For each pixel, the exemplary feature vector should be converted into a label. A number of exemplary machine learning classifiers can be used for this task. In one exemplary embodiment of the method according to the present disclosure, an exemplary support vector machine can be used, with a low-order polynomial kernel. This exemplary kernel can facilitate a non-linear classification behavior, while still operating in a relatively low dimensional space. The exemplary result of this classifier can be a label denoting whether the current pixel is classified as below or above the interface of interest. The exemplary classifier can be trained on example data coming from a set of manually segmented OCT scans of normal, healthy eyes.

Exemplary Regularization

The exemplary process of pixel classification can lead to a volume of pixels with class labels. These exemplary labels can denote that, according to the exemplary classification procedure, the pixel can be above or below the interface of interest. The exemplary classification result can contain some errors, possibly resulting in incorrectly assigned labels. In addition, imaging artifacts can lead to misclassified A-lines and registration errors result in discontinuities in the interface. Using every change in labels as the interface can possibly result in an unrealistic morphology of the layers. Instead, according to certain exemplary embodiments of the present disclosure, the detected interface can be be regularized by applying some constraints. By penalizing the curvature of the interface, its smoothness can be controlled.

One exemplary way of doing this can be by using exemplary level set methods, which can provide a non-parametric way to describe the interface. In contrast with parametric methods, such as snakes that provide an explicit parameterization of the interface, the exemplary level set methods can embed the interface implicitly, which can have certain computational advantages (e.g., regarding propagation and topology of the interface). The exemplary level set function φ can be defined in the same space as the input data (which is three dimensional for volumetric OCT data) and maps an input coordinate x to a scalar. The interface can then be defined as the curve C for which the level set is zero: C={ x|φ( x)=0}. The exemplary level set can be evolved according to the general level set equation

φ_(t) =−F|∇φ|.   (27)

φ_(t) is the update step of the level set, F is some force that drives the level set and ∇φ is the gradient of the level set. Adding the smoothness constraint (based on the curvature κ, which can be calculated directly from the level set function by

$\left. {\kappa = {\nabla{\cdot \left( \frac{\nabla\varphi}{\left| {\nabla\varphi} \right|} \right)}}} \right)$

and defining F by the label field L( x) results in

φ_(t) =ακ|□φ|−βL( x )|∇φ|,   (28)

where the ratio of α and β define the relative contributions of both terms. The label field L is produced by the classification routine explained in the previous section.

Exemplary Selective Combining

For each exemplary OCT backscatter measurement, the exemplary segmentation procedure (block 420) can produce a label (e.g., segmented data in block 430) denoting whether or not that pixel belongs to the tissue type(s) of interest. For each A-line, those measurements are retained which correspond to the selected tissue type(s). The exemplary resulting data of that A-line is then analyzed (e.g., block 440 in FIG. 4) to produce, e.g., a single value that describes some property of that data (e.g., block 450 in FIG. 4), e.g., its average or variance. By repeating such exemplary process for each A-line, a set of values can be produced that may be mapped to the locations at which the A-lines are acquired. Such exemplary values can then be displayed as a plot (e.g., for line scans) or as an image (e.g., for raster scans).

Although taking a single value for each set of selected points on an A-line can be one of the exemplary straightforward approaches, it should be understood that multiple values can be derived as well, e.g. average and variance, or percentile values. In addition, the selected points of neighboring A-lines can be combined to obtain a larger sample and therefore to obtain, e.g., a more reliable estimate of the statistic.

Exemplary Normalization

Exemplary power of the incident light that reaches the tissue can depend on various parameters, such as light source power, media opacities, interaction with other tissue layers etc. These exemplary parameters may not be known in advance, can change over time, and some parameters may be different for each patient or even each location. After an interaction with the tissue, the intensity of the backscattered light can again be affected by similar parameters.

The use of the exemplary normalization of the measured data (e.g., block 460 of FIG. 4) can therefore be preferred before the data can be analyzed quantitatively. One exemplary way of performing this normalization can be, e.g., by calculating the signal relatively to a reference signal. This exemplary reference signal can be provided by the backscatter of an unaffected retinal layer. In case of glaucoma, the RPE can be used as such a reference layer. For example, in every OCT A-line, the measured RNFL signal can be divided by the average RPE signal to produce the normalized RNFL signal. Additional properties of the reference layer can be used to produce a better reference signal. For example, the RPE signal can be averaged over multiple A-lines when assuming that the signal intensity does not change significantly for small lateral displacements.

Exemplary Numerical Output

The backscatter data that is combined per A-line can be further analyzed (e.g., block 470 of FIG. 4) to provide additional information about the selected tissue(s). For example, in glaucoma, the RNFL does not only get thinner, it also usually shows reduced backscatter. Based on data from healthy eyes, normative data for the backscatter properties of the RNFL can be derived. When analyzing new data, the backscatter properties can then be compared to the normative data to easily assess whether the observed RNFL backscatter is within or outside the normal range. One advantage over normative data as conventionally derived from thickness data can be that the tissue type(s) may have homogeneous backscattering properties across the whole scan, while the thickness often depends on the specific location. This reduces the normal range of observed values, and may thus result in a more sensitive diagnostic system and procedure.

First Exemplary Review

Exemplary images of the peripapillary areas were analyzed at four fixed locations of 10 normal and 30 glaucomatous eyes (e.g., mild, moderate and advanced glaucoma, 10 eyes each), scanned with the Spectralis OCT system. The reflectivity of the RNFL and GCIPL was measured relative to the retinal pigment epithelium. Differences in reflectivity between normal and glaucomatous eyes were explored. It was determined that the reflectivity of the RNFL was significantly lower in glaucomatous eyes than in normal eyes (e.g., p=0.018 in mild, p=0.001 in moderate and p<0.001 in advanced glaucomatous eyes). There was no significant difference in the reflectivity of the GCIPL between normal and glaucomatous eyes. The ratio between the RNFL and the GCIPL was significantly lower in glaucomatous eyes than in normal eyes (p<0.001 for mild, moderate and advanced glaucomatous eyes).

Image Acquisition and Processing—The participants were scanned once with the Spectralis OCT system. The peripapillary areas of both eyes were scanned by means of a volume scan of 20 by 20 degrees. This scan contained about 193 B-scans, each consisting of about 512 A-scans. Each B-scan was an average of 5 B-scans on the same location (e.g., using the eye-tracking system on the Spectralis OCT System with ART value 5). The lateral distance between every B-scan and the next one was about 30 μm. A volume scan would be excluded, if the proprietary overall quality score was below 15 dB or if the volume scan was incomplete due to the built-in maximum acquisition time of 300 seconds.

To determine the reflectivity, the raw data was utilized which represent the measured intensity on a linear scale between 0 and 1. The exemplary device can display these intensities in a grayscale image after applying the following formula: Y=⁴√X; where X represents the raw data values; Y=0 represents no reflectivity and Y=1 maximum reflectivity²⁸.

The exemplary analysis of the reflectivity was performed on all 4 quadrants around the optic nerve head (ONH) at fixed locations. The centre of the ONH was manually selected, taking the border of the retinal pigment epithelium as a reference. From this centre of the ONH, the areas of interest were selected 1.3 mm superiorly, inferiorly, temporally and nasally. To correct for any failed B-scans, the best out of 3 B-scans closest to the selected distance was selected for analysis. Per B-scan, an area for analysis of 20 pixels wide was selected. If the selected area of 20 pixels contained any blood vessels, a 20 pixels wide window was shifted along the. B-scan toward an area without blood vessels, to avoid any scatter caused by the blood vessel,

To obtain the reflectivity of each retinal layer separately, the layers were then manually segmented and color coded by a manual segmentation tool, ITKSNAP. The color coding was manually performed by a trained physician. The reflectivity of the RNFL, the GCIPL (Ganglion Cell Layer (GCL)+Inner Plexiform Layer (IPL)) and the ratio between the RNFL and the GCIPL, were used for analysis. The reflectivity of the GCL together with the IPL was determined, because of their resemblance in reflectivity. To correct for differences in optical properties, the reflectivity of the RNFL and the GCIPL were both expressed as a ratio of the retinal pigment epithelium (RPE).

Exemplary Results of First Exemplary Review

An exemplary spectral-domain(SD) OCT image of a normal eye (510) and of a glaucomatous eye (520) are shown in FIGS. 5A and 5B, respectively. In FIGS. 5A and 5B, the greyscale of the RNFL in the glaucomatous eye can be compared to the normal eye, representing the diminished reflectivity in the glaucomatous eye. The exemplary results on the reflectivity are shown in the graphs of FIGS. 6A and 613, In particular, FIG. 6A illustrates a graph presenting the reflectivity of the RNFL (retinal nerve fiber layer) and the GCIPL (ganglion cell layer+inner plexiform layer) for normal, mild, moderate and advanced glaucomatous eyes; presented as a mean (thick lines) and for each location separately, i.e. temporally, superiorly, nasally and inferiorly to the optic nerve head. FIG. 6B shows a graph presenting the ratio between the RNFL and the GCIPL for the same. The exemplary reflectivity provided in FIG. 6A is expressed as a ratio with respect to the reflectivity of the RPE (retinal pigment epithelium).

As shown in FIGS. 6A and 6B, the normalized mean reflectivity of the RNFL was lower in the glaucomatous eyes than in normal eyes. The normalized mean reflectivity decreased as the glaucoma became more severe, showing a change from about 1.53 to 0.85 between normal and mild glaucoma, dropping further to 0.65 for moderate and to 0.53 for advanced glaucoma. In the GCIPL, no statistically significant difference in reflectivity was found between the glaucomatous and normal eyes. The ratio between the RNFL and the GCIPL was significantly lower in the glaucomatous eyes than in normal eyes. These differences are illustrated in the exemplary images of FIGS. 5A and 5B.

The exemplary reflectivity of the RNFL and GCIPL of the patients was measured at four predetermined locations around the ONH, i.e. temporally, superiorly, nasally and inferiorly. The reflectivity of the RNFL and consecutively the ratio between the RNFL and GCIPL, was lower nasally than in the other locations, as provided in the graphs of FIGS. 6A and 6B.

Second Exemplary Review

An exemplary method according to an exemplary embodiment of the present disclosure can produce a numerical output from the data is by averaging the normalized RNFL reflectivity over an area. In this exemplary case, a band around the optic nerve head was used. For 10 normal eyes and 8 glaucomatous eyes, this average was calculated and the results are shown in the graph of FIG. 7. Note the large difference between the groups. The overlap of the distributions for both groups can be rather small, indicating that it can be used as a diagnostic tool. Excluding areas with blood vessels and using more advanced measures than a simple average can further improve the exemplary results. In addition, analysis can also be done on only a part of the data. In the graph of FIG. 8, the exemplary distribution of the average is shown for the superior, nasal, inferior and temporal quadrants.

Exemplary Graphical Output

An exemplary value for each selectively combined A-line can be displayed (e.g., block 480 of FIG. 4) for a better interpretation by selecting an exemplary display method that can match the transversal location of the corresponding A-lines. For scans along a straight line, a conventional plot can be used. For circular scans centered on the papilla, an exemplary TSNIT plot can be used. In this type of the exemplary plot, an angle (with respect to the papilla) can be plotted along the x-axis, and the corresponding value can be shown along the y-axis. The x-axis can be selected such that it starts temporally, proceeds through the superior, nasal and inferior regions and ends temporally. For raster-scans, an exemplary image can be produced, where each x and y position correspond to the location of the raster points and the value is shown in gray scale or in a false color map. When multiple values per A-line are derived, such exemplary values can be displayed.

In FIG. 9, example images of normalized RNFL reflectivity maps are shown of both normal and glaucomatous eyes using the exemplary embodiments of the systems, methods and computer-accessible medium according to the present disclosure. The examples of the glaucomatous eyes illustrate the visibility of localized defects. For example, the examples of normalized RNFL reflectivity maps of normal are provided on the top row of the images, and glaucomatous eyes are provided at the bottom row of the images. The RNFL reflectivity was normalized on the RPE. In the glaucomatous eyes, there are visible areas of reduced reflectivity (e.g., darker area's) stretching out from the ONH to the left side of the respective image.

Exemplary System

FIG. 10 shows an exemplary diagram of an exemplary embodiment of a system according to the present disclosure. For example, exemplary procedures in accordance with the present disclosure described herein can be performed by a processing arrangement and/or a computing arrangement 102. Such processing/computing arrangement 102 can be, e.g., entirely or a part of, or include, but not limited to, a computer/processor 104 that can include, e.g., one or more microprocessors, and use instructions stored on a computer-accessible medium (e.g., RAM, ROM, hard drive, or other storage device).

As shown in FIG. 4, e.g., a computer-accessible medium 106 (e.g., as described herein above, a storage device such as a hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) can be provided (e.g., in communication with the processing arrangement 102). The computer-accessible medium 106 can contain executable instructions 108 thereon. In addition or alternatively, a storage arrangement 110 can be provided separately from the computer-accessible medium 106, which can provide the instructions to the processing arrangement 102 so as to configure the processing arrangement to execute certain exemplary procedures, processes and methods, as described herein above, for example.

Further, the exemplary processing arrangement. 102 can be provided with or include an input/output arrangement 114, which can include, e.g., a wired network, a wireless network, the internet, an intranet, a data collection probe, at least one sensor, etc. The input/output arrangement can receive information/data from an OCT system 150 to provide information to the processing arrangement 102. Using such information received from the OCT system 150, the exemplary processing arrangement 102 can be configured to execute instructions to determine depth-resolved physical or optical properties of scattering media by analyzing measured data over a range of depths As shown in FIG. 4, the exemplary processing arrangement 102 can be in communication with an exemplary display arrangement 112, which, according to certain exemplary embodiments of the present disclosure, can be a touch-screen configured for inputting information to the processing arrangement in addition to outputting information from the processing arrangement, for example. Further, the exemplary display 112 and/or a storage arrangement 110 can be used to display and/or store data in a user-accessible format and/or user-readable format.

The foregoing merely illustrates the principles of the disclosure. Various modifications and alterations to the described embodiments will be apparent to those skilled in the art in view of the teachings herein. Indeed, the arrangements, systems and methods according to the exemplary embodiments of the present disclosure can be used with and/or implement any OCT system, OFDI system, SD-OCT system or other imaging systems, and for example with those described in International Patent Application PCT/US2004/029148, filed Sep. 8, 2004 which published as International Patent Publication No. WO 2005/047813 on May 26, 2005, U.S. patent application Ser. No. 11/266,779, filed Nov. 2, 2005 which published as U.S. Patent Publication No. 2006/0093276 on May 4, 2006, and U.S. patent application Ser. No. 10/501,276, filed Jul. 9, 2004 which published as U.S. Patent Publication No. 2005/0018201 on Jan. 27, 2005, and U.S. Patent Publication No. 2002/0122246, published on May 9, 2002, the disclosures of which are incorporated by reference herein in their entireties. It will thus be appreciated that those skilled in the art will be able to devise numerous systems, arrangements, and procedures which, although not explicitly shown or described herein, embody the principles of the disclosure and can be thus within the spirit and scope of the disclosure. In addition, all publications and references referred to above can be incorporated herein by reference in their entireties. It should be understood that the exemplary procedures described herein can be stored on any computer accessible medium, including a hard drive, RAM, ROM, removable disks, CD-ROM, memory sticks, etc., and executed by a processing arrangement and/or computing arrangement which can be and/or include a hardware processors, microprocessor, mini, macro, mainframe, etc., including a plurality and/or combination thereof. In addition, certain terms used in the present disclosure, including the specification, drawings and claims thereof, can be used synonymously in certain instances, including, but not limited to, e.g., data and information. It should be understood that, while these words, and/or other words that can be synonymous to one another, can be used synonymously herein, that there can be instances when such words can be intended to not be used synonymously. Further, to the extent that the prior art knowledge has not been explicitly incorporated by reference herein above, it can be explicitly being incorporated herein in its entirety. All publications referenced above can be incorporated herein by reference in their entireties. 

1. A method for determining at least one property of at least one biological structure, comprising: (a) obtaining a plurality of signals received at particular depths within the at least one biological structure, wherein at least first one of the signals is obtained from a first depth of the particular depths, and at least second one of the signals is obtained from a second depth of the particular depths, the first and second depths being different from one another; (b) determining information based on the signals and an assumed property of the at least one biological structure; and (c) determining at least one calculated property based on the information by excluding at least a portion of the information associated with the signals provided from the particular depths that are closer than a predetermined depth within the at least one biological structure, wherein the calculated property is an attenuation property.
 2. The method according to claim 1, further comprising: (d) repeating procedures (b) and (c) at least once, such that, in procedure (b), the assumed property is replaced with the calculated property of procedure (c) to obtain the at least one property of the at least one biological structure.
 3. The method according to claim 1, wherein the signals are optical coherence tomography signals or ultrasound signals.
 4. The method according to claim 1, wherein the at least one calculated property includes local optical properties of scattering media of the at least one biological structure, and wherein the local optical properties are determined using the information from a range of the particular depths.
 5. The method according to claim 4, wherein the local optical properties are determined using the information obtained from the depth which is a shallower depth and the information obtained at the second depth which is a larger depth.
 6. The method according to claim 5, further comprising summing the information obtained from the second depth to obtain an estimate of an intensity of a radiation forwarded to the at least one structure,
 7. The method according to claim 1, wherein the information is a local backscattered energy from the at least one structure.
 8. The method according to claim 7, wherein the local backscattered energy is measured by an optical coherence tomography procedure.
 9. The method according to claim 4, wherein the local optical property includes an attenuation coefficient.
 10. The method according to claim 4, wherein the at least one calculated property includes at least one optical property or at least one physical property which are iteratively determined from the information to be estimate at various depths within the at least one structure.
 11. The method according to claim 4, wherein the at least one calculated property is used for diagnosis or for at least one of a manual segmentation or an automatic segmentation.
 12. The method according to claim 1, wherein the at least one calculated property includes a calculated attenuation.
 13. The method according to claim 1, wherein the assumed property is an assumed attenuation.
 14. A system for determining at least one property of at least one biological structure, comprising: at least one first arrangement which is configured to obtain a plurality of signals received at particular depths within the at least one biological structure, wherein at least first one of the signals is obtained from a first depth of the particular depths, and at least second one of the signals is obtained from a second depth of the particular depths, the first and second depths being different from one another; and at least one second computing arrangement which is configured to determine: information based on the signals and an assumed property of the at least one biological structure; and at least one calculated property based on the information by excluding at least a portion of the information associated with the signals provided from the particular depths that are closer than a predetermined depth within the at least one biological structure, wherein the at least one calculated property is an attenuation property,
 15. The system according to claim 14, wherein the at least one second computing arrangement is further configured to: (d) repeat procedures (h) and (c) at least once, such that, in procedure (b), the at least one second computing arrangement causes a replacement of the assumed property with the calculated property of procedure (c) to obtain the at least one property of the at least one biological structure,
 16. The system according to claim 14, wherein the signals are optical coherence tomography signals or ultrasound signals.
 17. The system according to claim 14, wherein the at least one calculated property includes local optical properties of scattering media of the at least one biological structure, and wherein the at least one second computing arrangement is further configured to determine the local optical properties using the information from a range of the particular depths.
 18. The system according to claim 17, wherein the at least one second computing arrangement is configured to determine the local optical properties using the information obtained from the depth which is a shallower depth and the information obtained at the second depth which is a larger depth.
 19. The system according to claim 18, wherein the at least one second computing arrangement is further configured to sum the information obtained from the second depth to obtain an estimate of an intensity of a radiation forwarded to the at least one structure.
 20. The system according to claim 14, wherein the information is a local backscattered energy from the at least one structure.
 21. The system according to claim 20, wherein the local backscattered energy is measured by an optical coherence tomography procedure,
 22. The system according to claim 17, wherein the local optical property includes an attenuation coefficient.
 23. The system according to claim 17, wherein the at least one calculated property includes at least one optical property or at least one physical property, and wherein the at least one second computing arrangement is further configured to iteratively the at least one calculated property from the information to be estimate at various depths within the at least one structure.
 24. The system according to claim 17, wherein the at least one second computing arrangement is further configured to use the at least one calculated property for diagnosis or for at least one of a manual segmentation or an automatic segmentation,
 25. The system according to claim 14, wherein the at least one calculated property includes a calculated attenuation.
 26. The system according to claim 14, wherein the assumed property is an assumed attenuation.
 27. A non-transitory computer accessible medium which includes software thereon for determining at least one property of at least one biological structure, wherein, when a computing arrangement executes the software, the computing arrangement is configured to perform procedures comprising: (a) cause a plurality of signals received at particular depths within the at least one biological structure to be received, wherein at least first one of the signals is obtained from a first depth of the particular depths, and at least second one of the signals is obtained from a second depth of the particular depths, the first and second depths being different from one another; (b) determine information based on the signals and an assumed property of the at least one biological structure; and (c) determine at least one calculated property based on the information by excluding at least a portion of the information associated with the signals provided from the particular depths that are closer than a predetermined depth within the at least one biological structure, wherein the calculated property is an attenuation property. 